35 citations · 44 across the 10 of their papers we have counts for
13 papers
ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents
David Peer, Sebastian Stabinger
Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent limitations in trustworthiness, includin…
ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
David Peer, Philemon Schöpf, Volckmar Nebendahl +2
Traditionally, discriminative models have been the predominant choice for tasks like document classification and information extraction. These models make predictions that fall int…
Affordance detection with Dynamic-Tree Capsule Networks
Antonio Rodríguez-Sánchez, Simon Haller-Seeber, David Peer +3
Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neu…
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization
David Peer, Bart Keulen, Sebastian Stabinger +2
Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometime…
Momentum Capsule Networks
Josef Gugglberger, David Peer, Antonio Rodríguez-Sánchez
Capsule networks are a class of neural networks that achieved promising results on many computer vision tasks. However, baseline capsule networks have failed to reach state-of-the-…
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing
David Peer, Sebastian Stabinger, Stefan Engl +1
Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suff…